Artículos de revistas
Orientation-Independent Empirical Mode Decomposition for Images Based on Unconstrained Optimization
Fecha
2016-03Registro en:
Colominas, Marcelo Alejandro; Humeau Heurtier, Anne; Schlotthauer, Gaston; Orientation-Independent Empirical Mode Decomposition for Images Based on Unconstrained Optimization; Institute of Electrical and Electronics Engineers; Ieee Transactions on Image Processing; 25; 5; 3-2016; 2288-2297
1057-7149
CONICET Digital
CONICET
Autor
Colominas, Marcelo Alejandro
Humeau Heurtier, Anne
Schlotthauer, Gaston
Resumen
This paper introduces a 2D extension of the empirical mode decomposition (EMD), through a novel approach based on unconstrained optimization. EMD is a fully data-driven method that locally separates, in a completely data-driven and unsupervised manner, signals into fast and slow oscillations. The present proposal implements the method in a very simple and fast way, and it is compared with the state-of-the-art methods evidencing the advantages of being computationally efficient, orientation-independent, and leads to better performances for the decomposition of amplitude modulated-frequency modulated (AM-FM) images. The resulting genuine 2D method is successfully tested on artificial AM-FM images and its capabilities are illustrated on a biomedical example. The proposed framework leaves room for an nD extension (n > 2 ).